https://arxiv.org/abs/2203.08913

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Computer Science > Machine Learning

arXiv:2203.08913 (cs)
[Submitted on 16 Mar 2022]

Title:Memorizing Transformers

Authors:Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian
Szegedy
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    Abstract: Language models typically need to be trained or
    finetuned in order to acquire new knowledge, which involves
    updating their weights. We instead envision language models that
    can simply read and memorize new data at inference time, thus
    acquiring new knowledge immediately. In this work, we extend
    language models with the ability to memorize the internal
    representations of past inputs. We demonstrate that an
    approximate kNN lookup into a non-differentiable memory of recent
    (key, value) pairs improves language modeling across various
    benchmarks and tasks, including generic webtext (C4), math papers
    (arXiv), books (PG-19), code (Github), as well as formal theorems
    (Isabelle). We show that the performance steadily improves when
    we increase the size of memory up to 262K tokens. On benchmarks
    including code and mathematics, we find that the model is capable
    of making use of newly defined functions and theorems during test
    time.

Comments: Published as a conference paper at ICLR 2022 (spotlight)
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Computation and Language (cs.CL)
Cite as:  arXiv:2203.08913 [cs.LG]
          (or arXiv:2203.08913v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2203.08913
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yuhuai(Tony) Wu [view email]
[v1] Wed, 16 Mar 2022 19:54:35 UTC (1,168 KB)
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